{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4697ad5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1  载入分析所需要的模块和函数\n",
    "import pandas as pd#载入pandas模块，并简称为pd\n",
    "import matplotlib.pyplot as plt#载入matplotlib.pyplot模块，并简称为plt\n",
    "from sklearn.linear_model import LinearRegression#载入LinearRegression模块 这个是线性拟合用的模块\n",
    "from sklearn.model_selection import train_test_split#载入train_test_split模块  这是一个用于将数据集划分为训练集和测试集的常用方法\n",
    "from sklearn.metrics import mean_squared_error, r2_score#载入mean_squared_error, r2_score模块 分别用于计算均方误差和决定系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "abde3508",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   year    25 non-null     int64  \n",
      " 1   profit  25 non-null     float64\n",
      " 2   invest  25 non-null     float64\n",
      " 3   labor   25 non-null     int64  \n",
      " 4   rd      25 non-null     float64\n",
      "dtypes: float64(3), int64(2)\n",
      "memory usage: 1.1 KB\n",
      "\n",
      "5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "year      0\n",
       "profit    0\n",
       "invest    0\n",
       "labor     0\n",
       "rd        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2  数据读取及观察\n",
    "data=pd.read_csv('3.1.csv')\n",
    "data.info()\n",
    "print(len(data.columns) )\n",
    "\n",
    "data.columns # 本命令的含义是列出数据集中的变量\n",
    "data.shape # 列出数据集的形状 \n",
    "data.dtypes # 观察数据集中各个变量的数据类型\n",
    "data.isnull().values.any() # 检查数据集是否有缺失值 True表示有缺失值\n",
    "data.isnull().sum() # 逐个变量检查数据集是否有缺失值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "444fdc30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>profit</th>\n",
       "      <th>invest</th>\n",
       "      <th>labor</th>\n",
       "      <th>rd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>54.166667</td>\n",
       "      <td>7.949667e+04</td>\n",
       "      <td>1.105175e+04</td>\n",
       "      <td>8.139458e+03</td>\n",
       "      <td>8.439992e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>79496.666667</td>\n",
       "      <td>1.235536e+08</td>\n",
       "      <td>1.746151e+07</td>\n",
       "      <td>1.186445e+07</td>\n",
       "      <td>1.341083e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest</th>\n",
       "      <td>11051.750000</td>\n",
       "      <td>1.746151e+07</td>\n",
       "      <td>2.501602e+06</td>\n",
       "      <td>1.646000e+06</td>\n",
       "      <td>1.913827e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor</th>\n",
       "      <td>8139.458333</td>\n",
       "      <td>1.186445e+07</td>\n",
       "      <td>1.646000e+06</td>\n",
       "      <td>1.228873e+06</td>\n",
       "      <td>1.256714e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rd</th>\n",
       "      <td>8439.991667</td>\n",
       "      <td>1.341083e+07</td>\n",
       "      <td>1.913827e+06</td>\n",
       "      <td>1.256714e+06</td>\n",
       "      <td>1.482940e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year        profit        invest         labor            rd\n",
       "year       54.166667  7.949667e+04  1.105175e+04  8.139458e+03  8.439992e+03\n",
       "profit  79496.666667  1.235536e+08  1.746151e+07  1.186445e+07  1.341083e+07\n",
       "invest  11051.750000  1.746151e+07  2.501602e+06  1.646000e+06  1.913827e+06\n",
       "labor    8139.458333  1.186445e+07  1.646000e+06  1.228873e+06  1.256714e+06\n",
       "rd       8439.991667  1.341083e+07  1.913827e+06  1.256714e+06  1.482940e+06"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3 描述性分析\n",
    "data.describe()#对数据集进行描述性分析\n",
    "\n",
    "data.describe().round(2)#只保留两位小数\n",
    "data.describe().round(2).T#只保留两位小数并转置\n",
    "\n",
    "data.mean()#对数据集中的变量求均值 这个是对每一列的所有数据求\n",
    "data.var()#对数据集中的变量求方差\n",
    "data.std()#对数据集中的变量求标准差\n",
    "data.cov()#对数据集中的变量求协方差矩阵\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "410b931f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4  图形绘制：散点图和点线图\n",
    "plt.figure(figsize=(12,6))#设定图形的宽为12英寸，图形的高为6英寸\n",
    "plt.subplot(1,3,1)#指定作图位置。在同一画面创建1行3列个图形位置，首先在从左到右的第一个位置作图\n",
    "plt.scatter(data['invest'], data['profit'], alpha=0.6)#绘制invest和profit的散点图，使用数据集为data，x横轴为invest，y纵轴为profit，参数hue的作用就是在图像中将输出的散点图按照hue指定的变量（invest）的颜色种类进行区分,alpha为散点的透明度，取值为0到1\n",
    "plt.title(\"Scatter plot\")#将散点图的标题设定为Scatter plot\n",
    "\n",
    "plt.subplot(1,3,2)#指定作图位置\n",
    "plt.plot(data['invest'], data['profit'])#绘制invest和profit的线图\n",
    "plt.title(\"Line plot of invest, profit\")#将标题设定为Line plot of invest, profit\n",
    "\n",
    "plt.subplot(1,3,3)#指定作图位置\n",
    "plt.plot(data)#绘制invest和profit的线图\n",
    "plt.title('Line Plot')#将标题设定为Line Plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "69385ca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17, 2) (8, 2) (17, 1) (8, 1)\n",
      "[[3.46263171 6.060433  ]]\n",
      "\n",
      "(8, 1)\n",
      "992543.8152182677\n",
      "0.9929927630408805\n"
     ]
    }
   ],
   "source": [
    "#5. 使用 sklearn 进行线性回归\n",
    "#5.1  使用验证集法进行模型拟合\n",
    "X = data.iloc[:, 3:5]#将数据集中的第4列、第5列作为自变量\n",
    "y = data.iloc[:, 1:2] #将数据集中的第2列作为因变量 \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)#将样本示例全集划分为训练样本和测试样本，测试样本占比为30%。\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)#观察四个数据的形状\n",
    "\n",
    "model = LinearRegression()#使用线性回归模型\n",
    "model.fit(X_train, y_train)#基于训练样本拟合模型\n",
    "print(model.coef_)#计算上步估计得到的回归系数值\n",
    "model.score(X_test, y_test)#观察模型在测试集中的拟合优度（可决系数） 这些指标都是在0和1之间，越接近1表示模型性能越好\n",
    "\n",
    "pred = model.predict(X_test)#计算响应变量基于测试集的预测结果\n",
    "print(pred.shape)#观察数据形状\n",
    "print(mean_squared_error(y_test, pred))#计算测试集的均方误差\n",
    "print(r2_score(y_test, pred))#计算测试集的可决系数\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1fcdb39b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "913707.2575857218\n",
      "0.9927731937427586\n"
     ]
    }
   ],
   "source": [
    "#5.2  更换随机数种子，使用验证集法进行模型拟合\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)#更换随机数种子\n",
    "model = LinearRegression().fit(X_train, y_train)#基于训练样本拟合线性回归模型\n",
    "pred = model.predict(X_test)#计算响应变量基于测试集的预测结果\n",
    "print(mean_squared_error(y_test, pred))#计算测试集的均方误差\n",
    "print(r2_score(y_test, pred))#计算测试集的可决系数\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f881ccb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.88764934 0.99357787 0.97127709 0.98949747 0.99885212 0.99909274\n",
      " 0.98858874 0.99516552 0.8158804  0.96428546]\n",
      "0.9603866744663773\n",
      "0.05765271312564586\n",
      "[1184623.62833275  843657.38622967 2267922.27625153 1122050.16599988\n",
      "  208494.45434441   64604.15517951 1324424.57641146  832053.73135296\n",
      "  788171.99677182  311965.80146379]\n",
      "894796.8172337773\n",
      "936320.4383029323\n"
     ]
    }
   ],
   "source": [
    "#5.3  使用10折交叉验证法进行模型拟合\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import LeaveOneOut\n",
    "X = data.iloc[:, 3:5]#将数据集中的第4列至第5列作为自变量\n",
    "y = data.iloc[:, 1:2] #将数据集中的第2列作为因变量 \n",
    "\n",
    "model = LinearRegression()#使用线性回归模型\n",
    "kfold = KFold(n_splits=10,shuffle=True, random_state=1)#将样本示例全集分为10折 随机种子设为1\n",
    "\n",
    "scores = cross_val_score(model, X, y, cv=kfold)#计算每一折的可决系数\n",
    "print(scores)#显示每一折的可决系数\n",
    "\n",
    "print(scores.mean())#计算各折样本可决系数的均值\n",
    "print(scores.std())#计算各折样本可决系数的标准差 \n",
    "\n",
    "scores_mse = -cross_val_score(model, X, y, cv=kfold, scoring='neg_mean_squared_error')#得到每个子样本的均方误差\n",
    "print(scores_mse)#显示各折样本的均方误差\n",
    "print(scores_mse.mean())#计算各折样本均方误差的均值\n",
    "\n",
    "# 更换随机数种子，并与上步得到结果进行对比，观察均方误差MSE大小 均方误差（MSE）MSE的值越小，表示模型的预测效果越好。\n",
    "kfold = KFold(n_splits=10, shuffle=True, random_state=100)\n",
    "scores_mse = -cross_val_score(model, X, y, cv=kfold, scoring='neg_mean_squared_error')#得到每个子样本的均方误差\n",
    "print(scores_mse.mean())#计算各折样本均方误差的均值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6746b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "926095.7346274634"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5.4  使用留一交叉验证法进行模型拟合\n",
    "loo = LeaveOneOut()\n",
    "scores_mse = -cross_val_score(model, X, y, cv=loo, scoring='neg_mean_squared_error')\n",
    "scores_mse.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "117013aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.82571358]]\n",
      "[4.12671881]\n"
     ]
    }
   ],
   "source": [
    "# 第一题\n",
    "X = 2 * np.random.rand(100, 1)\n",
    "y = 4 + 3 * X + np.random.randn(100, 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)  #将样本示例全集划分为训练样本和测试样本，测试样本占比为30%。\n",
    "model = LinearRegression()#使用线性回归模型\n",
    "model_ = model.fit(X_train, y_train)#基于训练样本拟合模型\n",
    "print(model.coef_)#计算上步估计得到的斜率\n",
    "print(model.intercept_ )#计算上步估计得到的截距"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ecee90df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.82571358]]\n",
      "[4.12671881]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第二题\n",
    "def linear_regression(x, coefficients, intercept):  \n",
    "    return coefficients[0] * x + intercept  \n",
    "  \n",
    "# 拟合线性回归模型  \n",
    "coefficients = model_.coef_  \n",
    "intercept = model_.intercept_  \n",
    "\n",
    "print(coefficients)\n",
    "print(intercept)\n",
    "\n",
    "# 预测x=0.5时的值  \n",
    "X_pred = 0.5  \n",
    "y_pred = linear_regression(X_pred, coefficients, intercept)  \n",
    "  \n",
    "# 显示中文\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "# 支持符号\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 绘制数据点和拟合直线  \n",
    "plt.scatter(X, y, color='blue', label='数据点')  \n",
    "plt.plot(X.reshape(-1, 1), model.predict(X.reshape(-1, 1)), color='red', label='拟合线')  \n",
    "\n",
    "# 在图表中添加文本。\n",
    "plt.text(X_pred, y_pred, f'预测值X={X_pred}: {y_pred}', ha='center')  \n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel('$y$') \n",
    "plt.legend()  # 加载图例\n",
    "plt.grid()  # 绘制网格\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8700a59a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.82571358]]\n",
      "[4.12671881]\n"
     ]
    }
   ],
   "source": [
    "# 第三题\n",
    "import joblib\n",
    "# 保存模型  \n",
    "joblib.dump(model_, 'test.pkl')  \n",
    "  \n",
    "# 在需要的地方加载模型  \n",
    "model_loaded = joblib.load('test.pkl')\n",
    "# 拟合线性回归模型  \n",
    "coefficients = model_.coef_  \n",
    "intercept = model_.intercept_  \n",
    "\n",
    "print(coefficients)\n",
    "print(intercept)  # 有输出则表示成功加载模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f97a883",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c238179",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
